Cooperative localization using posterior linearization belief propagation

Angel F. Garcia-Fernandez, Lennart Svensson, Simo Särkkä

Research output: Contribution to journalArticleScientificpeer-review

14 Citations (Scopus)
151 Downloads (Pure)


This paper presents the posterior linearisation belief propagation (PLBP) algorithm for cooperative localisation in wireless sensor networks with nonlinear measurements. PLBP performs two steps iteratively: linearisation and belief propagation. At the linearisation step, the nonlinear functions are linearised using statistical linear regression with respect to the current beliefs. This SLR is performed in practice by using sigma-points drawn from the beliefs. In the second step, belief propagation is run on the linearised model. We show by numerical simulations how PLBP can outperform other algorithms in the literature.

Original languageEnglish
Pages (from-to)832-836
JournalIEEE Transactions on Vehicular Technology
Issue number1
Publication statusPublished - 2018
MoE publication typeA1 Journal article-refereed


  • Approximation algorithms
  • Bayes methods
  • Belief propagation
  • cooperative localisation
  • Covariance matrices
  • Gaussian message passing
  • Gaussian noise
  • Kalman filters
  • Message passing
  • Posterior linearisation
  • Sigma points


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